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. Author manuscript; available in PMC: 2026 Aug 7.
Published in final edited form as: Diabetes Res Clin Pract. 2025 Aug 7;227:112415. doi: 10.1016/j.diabres.2025.112415

Effects of Individual, Health System and Neighborhood Risks on Diabetes Health Outcomes Among Emerging Adults with Type 1 Diabetes

Deborah A Ellis a, April Idalski Carcone a, Colleen Buggs-Saxton b, Arti Bhan c, M Bassem Dekelbab d
PMCID: PMC12376852  NIHMSID: NIHMS2104226  PMID: 40782842

Abstract

Aims:

To test associations between individual, health system and neighborhood-level risk and protective factors, and health outcomes in a diverse sample of emerging adults (EAs) with type 1 diabetes (T1D).

Methods:

Data were drawn from the baseline data collection of a clinical trial. One hundred and thirteen EAs [47.8% White/non-Hispanic; mean age= 20.9 years; mean HbA1c =9.5% (IFCC = 81 mmol/mol)] completed self-report questionnaires on diabetes-self efficacy, diabetes distress, communication with diabetes health care provider, neighborhood crime and diabetes management. Structural equation modelling estimated the direct and indirect effects of individual, health care provider, and neighborhood factors on diabetes management and glycemic control.

Results:

In the final model, self-efficacy for diabetes care was the only significant predictor of diabetes management (β = .32, p < .001). Neighborhood crime (β = .17, p < .05) and diabetes management (β = −.28, p < .05) had significant direct effects on glycemic control, while diabetes self-efficacy had a significant indirect effect though diabetes management (β = −.090; p < .01).

Conclusions:

Individual factors such as higher self-efficacy for diabetes management and neighborhood factors such as lower crime rates have protective effects on the diabetes health of EAs with T1D.

Keywords: Type 1 Diabetes, Emerging Adults, Diabetes Management, Neighborhood Influences

1.0. Introduction

Emerging adulthood has been defined as the period from the late teens to the mid-late 20s [1, 2]. This developmental period is marked by numerous transitions into new roles, including those related to college and/or employment, development of new social networks, and need for increased independence from the family of origin [3]. Emerging adults (EAs) with type 1 diabetes (T1D) also face additional challenges related to their chronic condition, such as transitioning their medical care to adult health care providers (HCPs) [4] and maintaining adequate health care insurance [5] in order to avoid disruptions in care as well as access to medications and supplies. Therefore, it is not surprising to find that EAs have been shown to be at elevated risk for difficulties with diabetes management (DM) [6], including lower use of diabetes management technologies such as continuous blood glucose monitors and insulin pumps [7, 8]. EAs with T1D also have suboptimal glycemic control in comparison to children and older adults [7, 9]. For example, one recent longitudinal study showed a statistically and clinically significant 1.0% increase in hemoglobin A1c (HbA1c) in the two years after high school graduation in a sample of 236 EAs [10]. Studies to investigate the factors that influence DM and glycemic control in this high-risk age group are therefore needed.

The Diabetes Resilience Model [11] is a theoretical model which has been widely used to predict behavioral resilience (e.g. higher levels of DM) and health resilience (e.g., optimized glucose control) among youth with T1D. It proposes that risks and assets at the level of the individual, family and broader social context interact with one another, and therefore these various domains should be assessed when identifying processes that contribute to diabetes health outcomes.. Most studies of EAs have assessed such factors at either the level of the individual, their family or the health care system. At the level of the individual, diabetes distress (DD) [12] ), or negative affect and emotional burden that results from the challenges of living with diabetes, has been found to be common among EAs [13, 14]. DD can interfere with the completion of DM as well as result in higher blood glucose levels. [15]. However, given some studies with EAs have shown that DD affects DM, while others have shown only effects on glycemic control, the pathway by while DD exerts its effects on diabetes health is unclear [16]. Consistent with the developmental tasks of this age group, which include the successful transition to independence, EA’s self-efficacy and perceived competence for DM have also been shown to be an important predictor of both DM and glycemic control [17, 18]. At the level of the health-care system, quality of communication between EAs and their HCP has also been found to predict diabetes health; both “patient-centered communication” [19] and “autonomy-supportive communication” on the part of HCPs [20] are associated with better diabetes health outcomes.

While there are numerous studies on the effects of risk and protective factors on diabetes health outcomes among EAs with T1D, studies of the effects of neighborhood influences such as neighborhood-level SES/ adversity, availability of neighborhood resources such as healthy foods or greenspace, and residential segregation are lacking. One of the few studies investigating the influence of neighborhoods on the health of EAs found an association between higher levels of neighborhood disadvantage and higher HbA1c , although this relationship was not significant once family-level socio-economic status was included in the model [21]. No associations between neighborhood disadvantage and DM were found. In a two-year longitudinal study using the same sample of EAs, Mello and colleagues [22] found that higher levels of neighborhood disadvantage were predictive of lower family relationship quality, which in turn was associated with poorer DM and glycemic control. In light of the limited studies to date, it is unclear whether the effects of adverse neighborhood conditions on the diabetes health of EAs with T1D are conveyed through disruptions to DM (e.g., access to healthy food, opportunities for exercise) or reflect the effects of stressful environments in dysregulating the HPA axis, which in turn affects glycemic control. Stress-inducing aspects of neighborhoods, such as high rates of crime and violence, have been shown to be associated with glycemic control in adults with type 2 diabetes [23] and with BMI in adults with obesity through their effects on inflammatory pathways [24]. However, to date, the effects of neighborhood crime on health have not been explored among EAs with T1D. Recent calls for more focus on health equity in T1D research and clinical care also highlight the need to better understand the effects of neighborhood and community-level factors [25] on minoritized emerging adults with T1D. This group is at heightened risk for suboptimal diabetes outcomes [26]; in addition, minoritized EAs are disproportionately more likely to be living in adverse neighborhood conditions[27].

The aim of the present study was to use social-ecological theory to test associations between individual risk factors (diabetes distress and self-efficacy for diabetes care), health system risk factors (communication with diabetes health care providers), neighborhood risk factors (neighborhood crime) and health outcomes (DM and glycemic control) in a diverse sample of EAs with T1D. We hypothesized that higher levels of neighborhood crime would be associated with higher blood average glucose levels and would also be associated with blood glucose levels indirectly through lower diabetes management.

2.0. Subjects

Data for the present study were drawn from a clinical trial testing an eHealth, autonomy-supportive intervention to improve diabetes self-management among EAs with T1D and an elevated HbA1c. The trial was registered in clinicaltrials.gov (registration number NCT04066959). Data used in the analyses were drawn from the participant’s baseline data collection prior to study randomization or delivery of the intervention.

Participants were initially recruited from diabetes clinics located within health systems in Detroit, MI via letters introducing the study, followed by phone calls and texts from study research staff to assess interest in participation. Recruitment took place between 2019 and 2024. During the COVID-19 pandemic, the initial recruitment approach was expanded to allow for national recruitment through social media outreach and advertising through diabetes non-profit organizations such as the T1D Exchange [28]. In order to be eligible for the parent clinical trial, EAs had to be aged 16 years, 0 months through 25 years, 11 months, diagnosed with T1D for at least six months and have an elevated HbA1c based on the recommended target range for glycemic control in this age group (HbA1c ≥7.5% currently and on average over the previous 6 months) [29]. As the parent clinical trial required that EAs participate in an eHealth intervention that involved text messaging support and/or an eHealth intervention to address communication with health care providers, study participants were also required to have access to a mobile device where they could receive texts and to have a scheduled appointment with their diabetes health care provider within eight weeks of recruitment. EAs were excluded if 1) they had mental health conditions that precluded provision of informed consent or participation in independent diabetes management (i.e., thought disorders/psychosis, developmental delay, suicidality), 2) the diagnosis of T1D was secondary to another chronic medical illness (e.g., cystic fibrosis), or 3) they were not able to speak or read English. The research was approved by the IRB of the first author’s university using a single IRB agreement. All participants provided informed consent and/or assent to participate.

The sample size for the present study was 113 (all consented clinical trial participants who completed baseline data collection). Demographic characteristics of the participants are shown in Table 1. Participants were primarily female (69.9%) with a mean age of 20.9 (SD = 3.0). The sample was diverse, as 47.8% identified as White/non-Hispanic, 37.2 % identified as Black/non-Hispanic, 7.1% identified as Hispanic and 7.9% identified as other race/ethnicity. Mean HbA1c was 9.5% (81 mmol/mol), indicating that the sample’s glycemic control was outside of the recommended range, which is consistent with known elevations in blood glucose levels during this developmental window. The majority of EAs (55.8%) were managed with insulin pumps, while 45.2% used injected insulin.

Table 1:

Participant Demographic and Clinical Characteristics (N = 113)

Variable Mean ± SD n (%)
Age, in years 20.9 ± 3.0
Biological Sex Assigned at Birth
  Female 79 (69.9)
  Male 34 (30.1)
Race/ethnicity
  Black, non-Hispanic 42 (37.2)
  White, non-Hispanic 54 (47.8)
  Hispanic or Latino 8 (7.1)
  Other 9 (7.9)
Education, in years 12.7 ± 2.2
  ≤High school diploma or equivalent 67 (59.3)
  >High school 46 (40.7)
Residence
  In family home 91 (80.5)
  Living independently 22 (19.5)
Employment status
  Employed, full or part-time 75 (66.3)
  Unemployed 38 (33.6)
Subjective socioeconomic status 5.6 ± 1.8
  Duration of diabetes, in years 10.9 ± 5.2
  HbA1c
  % 9.5 ± 2.0
  mmol/mol 81 ± 22
  Blood glucose monitoring method
  Meter 85 (75.2)
  Continuous Glucose Monitor (CGM) 28 (24.8)
Insulin delivery method
  Injected Insulin 51 (45.2)
  Insulin infusion pump 63 (55.8)
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3.0. Materials and Methods

Prior to the COVID-19 pandemic, data collection visits were conducted in EAs’ homes to ease the burden of participation. Due to restrictions placed on face-to-face data collection during the pandemic, as well as the change to a national recruitment strategy, data collection visits after the start of pandemic were conducted remotely using a virtual platform. Questionnaire data were obtained throughout the study period using REDCap, an electronic data capture system. After the start of the pandemic, test kits used to obtain measures of glycemic control were hand delivered to local participants using a contactless drop off procedure or mailed to participants residing outside the local area. Participants were compensated with $50 for completing the data collection visit and an additional $10 for returning HbA1c test kits that were mailed.

3.1. Measures

Diabetes distress.

The Problem Areas in Diabetes (PAID) [30] is a widely used, 20-item self-report measure of diabetes-specific emotional distress that evaluates a wide range of feelings related to living with diabetes and its treatment, including guilt, anger, depressed mood, worry, and fear. Each question has five possible answers with a value from 0 to 4, with 0 representing “no problem” and 4 “a serious problem”. Higher scores indicate greater emotional distress. Internal consistency was very good in this sample (α = .94).

Diabetes management.

The Diabetes Management Scale (DMS) [31] is a self-report questionnaire designed to measure a broad range of diabetes management behaviors, such as insulin management, dietary management, blood glucose monitoring, and symptom response. Each item asks “What percent of the time do you (take your insulin)?” The response scale is 0-100%. A total score is obtained by calculating the mean response to all items to reflect overall management behavior; higher scores indicate higher levels of diabetes management. The DMS has been adapted for use with persons living with diabetes using intensive insulin regimens, with good internal consistency [32]. In the current sample, internal consistency was acceptable (α = .66).

Health care provider communication.

The communicative support subscale of the Health Care Climate Questionnaire (HCCQ) [33] was used to evalute communication with diabetes health care providers. Participants use a 7-point Likert scale to rate 6 items related to autonomy-supportive communication such as empathic listening, encouraging questions and offering treatment options. Higher scores reflect higher levels of autonomy-supportive communication; the scale has been shown to be associated with diabetes distress in adult samples with T1D [34]. Internal consistency was excellent in this sample (α = .94).

Glycemic control.

Hemoglobin A1c (HbA1c), a retrospective measure of average blood glucose during the past two to three months, was used to evaluate glycemic control. Values were obtained during data collection visits using the Accubase test kit (DTI Laboratories, Inc.), which is FDA approved and suitable for home use. High performance liquid chromatography (HPLC) is used to analyze the blood sample.

Neighborhood crime.

The perceived crime subscale of the Perceived Neighborhood Scale (PNS) [35] was used to evaluate subjective perceptions of neighborhood violence. The six-item subscale measures perceptions of threat, crime, and social disorder on a five-point Likert scale. Higher scores reflect lower perceived crime. The subscale has been shown to have strong psychometric qualities, including demonstrating associations with the individual’s desire to move from the area and objective measures of neighborhood poverty[ 35, 36]. It is also associated with health outcomes such as rates of pre-term birth [37]. In the current sample, internal consistency was acceptable (α = .68).

Self-efficacy for diabetes care was measured using the Perceived Health Competence Scale (PHCS) [38], an eight-item measure of the degree to which the individual feels capable of effectively managing their health. The measure was reworded to ask about self-efficacy for diabetes care specifically, rather than care of health in general. The measure has previously been shown to be reliable and valid as it is associated with health intentions and behavior including perceptions of dietary management [39]. Internal consistency was very good in this sample (α = .86).

Sociodemographic and Clinical Variables.

Information on demographic variables such as age, race/ethnicity, gender, and education was obtained from an investigator-developed questionnaire. Socioeconomic status (SES) was calculated using the MacArthur Scale of Subjective Socioeconomic Status (SSSS), a widely used scale of subjective social standing related to employment grade, education, household and personal income, household wealth, satisfaction with standard of living, and feelings of financial security [40]. The respondent selects the rung on a 10-rung ladder that represents their perception of their family’s social standing in society. The scale is coded such that higher scores indicate higher SES. The participant’s medical chart was reviewed to obtain clinical information such as duration of diabetes and insulin delivery method.

3.2. Analytic Approach

Bivariate analyses were initially conducted to assess simple relationships between variables using IBM SPSS Statistics Version 30. The effects of individual, health care provider and neighborhood risk and protective factors on diabetes management and glycemic control were tested via structural equation modeling (SEM) using IBM SPSS AMOS 30. Because multiple indicators were not available for most constructs, models were evaluated using path analysis, a form of SEM that uses all single indicator constructs. Path analysis is similar to ordinary least squares regression. However, it has the advantage of allowing both the assessment of goodness of fit of a specified model and testing of each estimated path coefficient. The theoretical model is shown in Figure 1. Based on the literature suggesting that diabetes self-efficacy, diabetes distress, quality of communication with the health care provider, and perceived neighborhood crime could have both direct effects on DM and glycemic control, as well as indirect effects on glycemic control through DM, we tested a fully specified model.

Figure 1:

Figure 1:

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Theoretical Model of Effects of Socio-Ecological Influences on Diabetes Health

4.0. Results

4.1. Bivariate Analyses

Results of bivariate analyses are shown in Table 2, along with descriptive statistics for model constructs. Neighborhood crime was significantly associated with age (r = .19, p < .05) race/ethnicity (r = .32, p < .001) and insulin delivery technology (r = .28, p < .01) indicating that older EAs, minoritized EAs and those using injected insulin rather than an insulin pump reported higher rates of neighborhood crime. Neighborhood crime was also significantly associated with HbA1c (r = .26, p < .01) suggesting that living in higher crime neighborhoods was associated with suboptimal glycemic control. There was no significant association between neighborhood crime and DM (r = −.12, ns).

Table 2.

Correlations Among Study Variables

1 2 3 4 5 6 7 8 9
1. Age (in years)
2. Race .04
3. Insulin Delivery .03 .41**
4. Problem Areas in Diabetes .25** .09 .07
5. Diabetes Management Scale −.01 −.18 −.22* −.37**
6. Health Care Climate Questionnaire −.06 −.14 −.01 −.22* .19*
7. Hemoglobin A1c −.17 .36** .45** .04 −.29** −.16
8. Perceived Neighborhood Crime .19* .32** .28** .21* −.12 −.22* .26**
9. Perceived Health Competence Scale −.08 −.06 .03 −.55** .40** .32** −.16 −.16
Mean - - - 36.2 67.4 3.86 9.5 2.44 3.14
SD - - - 21.7 15.9 0.95 2.0 0.61 0.71
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Note. Race reference group= White, non-Hispanic, Insulin delivery reference group=insulin pump

*

p ≤ .05

**

p ≤ .01

DM was significantly associated with diabetes self-efficacy (r = .40, p < .001), quality of health provider communication (r = .19, p < .05) and DD (r = −.37, p < .001), indicating that higher self-efficacy, better quality of communication with the HCP and lower DD were associated with better DM. HbA1c was significantly associated with DM (r = −.29, p < .01), such that those with higher average blood glucose levels reported poorer DM.

4.2. Path Analyses

A structural equation model with single indicator variables was fit to the variance/covariance matrix using a maximum likelihood solution to estimate the direct and indirect effects of individual, health care provider, and neighborhood risk and protective factors on diabetes management and glycemic control. The model had two exogenous variables (DM and glycemic control) and four endogenous variables (diabetes self-efficacy, diabetes distress, quality of communication with health care provider, and neighborhood crime). Three covariates (age, race/ethnicity, and insulin delivery method) were included based on their known associations with diabetes health outcomes in prior studies, which were also replicated in the present analyses (see Table 2). Three fit indices were evaluated: that the likelihood ratio X2 test of model fit was nonsignificant, the comparative fit index (CFI) was > .90, and the root mean square error of approximation (RMSEA) was < .08.

The initial, fully specified model did not fit the data well: X2(12, N = 113) = 28.54, p = .005; CFI = .89; RMSEA = .11 (Figure 2). To improve model fit, paths between exogenous and endogenous variables with p values <.10 were trimmed, as were nonsignificant covariances. Two additional covariances were added based on modification indices. The trimmed model fit the data well: X2(15, N = 113) = 24.63, p =.06; CFI = .93; RMSEA = .08. The results of a Bollen-Stine bootstrap analysis confirmed the model (p = .137). Figure 3 shows the final model with standardized path coefficients. In this model, only diabetes self-efficacy had a significant effect on diabetes management (β = .32, p < .001). Neighborhood crime (β = .17, p < .05) and diabetes management (β = −.28, p < .05) both exhibited significant direct effects on glycemic control. These effects were in the expected direction. In addition, the indirect effect of diabetes self-efficacy on glycemic control though diabetes management was also significant (β = −.090; SE = .044, p < .01).

Figure 2:

Figure 2:

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Initial Structural Equation Model. Race reference group= White, non-Hispanic, Insulin delivery reference group=insulin pump. + p <.10, *p <.05, **p < .01, ***p <.001

Figure 3:

Figure 3:

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Final Structural Equation Model. Race reference group= White, non-Hispanic, Insulin delivery reference group=insulin pump. + p <.10, *p <.05, **p < .01, ***p <.001

5.0. Discussion

The present study used the Diabetes Resilience Model to evaluate the effects of individual, health care system, and neighborhood level influences on diabetes health outcomes such as DM and glycemic control among EAs with T1D. In multivariate analyses, ratings of perceived diabetes health competence, which reflect self-efficacy for diabetes care, were the only significant predictor of diabetes management; neither diabetes distress nor communication with health care providers accounted for diabetes management outcomes. Findings are consistent with self-determination theory (SDT) [41] and the Health Belief Model (HBM) [42] which suggest that feelings of self-efficacy are a critical component of overall health and well-being and also with qualitative studies demonstrating the importance of self-efficacy as part of the process of developing independent skills for diabetes management among EAs with T1D[43]. Also consistent with SDT, higher self-efficacy was also associated with glycemic control indirectly through its relationship with higher DM.

The lack of association between DM and diabetes distress in the final model may suggest that EAs who feel empowered to care for their diabetes are more likely to complete a higher percentage of their care despite the emotional burden of their medical condition. While all EAs in the study had access to diabetes health care providers- and were required to be engaged in such care based on eligibility criteria for the parent clinical trial- possible explanations for the lack of association between DM and quality of communication with HCPs in the final model include the well-known challenges with transitions of care during this developmental period. EAs in the study may have been in the process of transitioning from pediatric to adult diabetes care settings and therefore could have been developing a relationship with a new HCP. Data on the frequency of clinic attendance, which might serve as a proxy for the degree to which EAs had developed a working relationship with their HCP, were not gathered as part of the present study.

The present study is one of the first to demonstrate the effects of neighborhood conditions on the glycemic control of EAs with T1D. In the present study, neighborhood crime was not associated with DM, but rather had a direct effect on glycemic control, with higher rates of perceived crime significantly associated with higher average blood glucose levels. Two prior studies have shown mixed results regarding the effects of neighborhood conditions on the health of EAs, with one showing that neighborhood disadvantage conveyed its effects on glycemic control via disruptions in family relationships and diabetes management [22] and the other showing only direct effects on glycemic control [21]. However, studies conducted with children and younger adolescents with T1D are supportive of the current findings suggesting that neighborhood conditions exert their effects not through their impact on the individual’s diabetes management but rather through other pathways that impact glycemic control such as psychosocial stress [44-46]. High rates of neighborhood crime in particular have been shown to be associated with psychosocial stress[47], which in turn could result in HPA axis dysregulation and increased blood glucose levels. Additional support for the possibility that neighborhood crime increases psychological stress, which in turn can affect insulin counter-regulatory hormones such as cortisol, comes from a recent study of Black adults showing that those residing in more violent neighborhoods had higher hair cortisol concentrations [48]. Although measures of general psychological stress or stressful life events were not obtained in the current study, future studies could directly assess whether such risk factors mediate the relationship between neighborhood exposures and glycemic outcomes.

Unlike prior studies investigating the effects of neighborhoods on persons living with T1D, the present study used a subjective measure of neighborhood quality rather than an objective measure of neighborhood context, such as those derived from US Census data. Findings of an association between self-reported neighborhood quality and a critical health outcome such as glycemic control suggests that conducting screening for SDOHs, such as crime in neighborhoods, via self-report measures could be a useful approach for health care providers to identify EAs at risk for adverse diabetes health outcomes[26]. Future studies should further explore the specific mechanisms by which neighborhood characteristics influence the health of EAs with TID.

A strength of the study was the diverse sample comprised of approximately 50% minoritized EAs who were recruited through a variety of methods from across the US. Research with EAs to date has focused on predominantly White samples, despite studies showing that minoritized EAs with T1D have worse diabetes health outcomes[49]. Minoritized EAs in the present study were more likely to report living in high crime neighborhoods and the present study provides evidence for the effects of residence in such neighborhoods on glycemic control. Limitations of the study include the cross-sectional design which precludes causal conclusions. In addition, participants were part of a clinical trial and therefore may have differed from the general population of EAs with T1D. For example, the requirement that the EA have an appointment with a diabetes health care provider to participate meant that all study participants were actively engaged in care; however, prior studies have shown that gaps in diabetes medical care are commonplace for EAs [50]. More than half of the sample identified as female, which may limit the generalizability of the findings for other genders. Finally, the use of a self-report measure of neighborhood crime may also have affected the study findings.

In summary, the results of the present study provide empirical support for a model in which both individual factors, such as self-efficacy for diabetes management, and neighborhood factors, such as low crime rates, have protective effects on the diabetes health of EAs with T1D. Additional studies are needed to better understand the potential reciprocal nature of such influences over time in order to develop effective, developmentally relevant interventions for emerging adults.

Acknowledgements

We would like to thank the study participants and the research staff at the investigational centers.

Funding

This work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health [R01DK116901]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declaration of interests statement: None.

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Data Availability

The data will be made available upon publication to researchers who provide a methodologically sound proposal for use under a specific data-sharing agreement that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate technology; and (3) a commitment to destroying the data after analyses are completed.

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Data Availability Statement

The data will be made available upon publication to researchers who provide a methodologically sound proposal for use under a specific data-sharing agreement that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate technology; and (3) a commitment to destroying the data after analyses are completed.

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